Image segmentation is often used to identify regions of interest for use in medical image analysis. In particular, image segmentation is used to segment structures from the background and is often used as a first step for medical image analysis, such as for visualization, quantitative image analysis, and image guided intervention.
Image segmentation can be difficult to perform because of the large variability of shape and appearance of different structures, including the lack of contrast between adjacent or neighboring structures. Known image segmentation methods are generally divided into image-based approaches and atlas-based approaches. For example, image-based approaches segment based on image cues including intensity, gradient, and/or texture. Image based methods use different models that perforin generally well when structures of interest have prominent boundaries and the intensities of neighboring structures are different. However, these methods often perform poorly when these conditions are not met. In particular, it is often difficult to incorporate prior anatomical knowledge into these image-based approaches especially when applied to multi-structure segmentation.
Atlas-based approaches rely largely on prior knowledge about the spatial arrangement of structures. These approaches typically include first registering one or more manually segmented images, called atlases, to the subject image, called target, so that the manual segmentations on the atlases are propagated and fused. Compared to image-based approaches, these methods incorporate anatomical knowledge for improved performance, but are limited by large anatomical variation and imperfect registration.
Weighted fusion methods have also been proposed to improve performance where the segmentation fusion is weighted based on the intensity similarity between the target and the atlas images. However, information about structure intensity and contour that is specific to the subject's anatomy is not used, which makes it difficult to apply these methods to subjects with large anatomical differences from the atlases. Other methods have also been proposed and include an adaptive atlas method that allows large structure variation based on target image intensities. However, adaptive atlas methods do not consider structure boundary information, which means these methods cannot discriminate different structures that have similar intensities. Still other proposed methods use spectral label fusion that divides the target image into regions based on image intensities and contours, followed by voting on the regions using an atlas-based approach. However these methods are usually limited to a single anatomical region and would be difficult to extend to segment multiple regions simultaneously.
Thus, known segmentation methods suffer from different drawbacks as a result of using such an image based approaches or an atlas-based approaches.